However, some issues still remain in current FL systems. Firstly, users need to fully trust the MEC server for model aggregation, but this is not always achieved in realistic wireless networks. Secondly, although FL can help enhance user privacy, the transmission of learning parameters to the MEC server is vulnerable to security bottlenecks such as malicious threats that can modify or steal the information of local updates [1]}. Moreover, the reliance of an MEC server for model aggregation introduces single-point-of-failure bottlenecks once the server is attacked, which consequently disrupts the entire FL system. Thirdly, given the high scalability of modern edge computing networks, a single MEC server cannot manage to aggregate all updates offloaded from millions of devices. Therefore, there is an urgent need to develop a more decentralized FL approach without using a central server so as to solve security and scalability issues for enabling the next generation intelligent edge networks.
